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An open source framework to build and scale your ML and Python applications easily

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Batch inference Model training Hyperparameter tuning Model serving Reinforcement learning

  from typing import Dict   import numpy as np    import ray    # Step 1: Create a Ray Dataset from in-memory Numpy arrays.   ds = ray.data.from_numpy(np.asarray(["Complete this", "for me"]))    # Step 2: Define a Predictor class for inference.   class HuggingFacePredictor:       def __init__(self):           from transformers import pipeline           # Initialize a pre-trained GPT2 Huggingface pipeline.           self.model = pipeline("text-generation", model="gpt2")        # Logic for inference on 1 batch of data.       def __call__(self, batch: Dict[str, np.ndarray]) -> Dict[str, list]:           # Get the predictions from the input batch.           predictions = self.model(               list(batch["data"]), max_length=20, num_return_sequences=1)           # `predictions` is a list of length-one lists. For example:           # [[{"generated_text": "output_1"}], ..., [{"generated_text": "output_2"}]]           # Modify the output to get it into the following format instead:           # ["output_1", "output_2"]           batch["output"] = [sequences[0]["generated_text"] for sequences in predictions]           return batch    # Use 2 parallel actors for inference. Each actor predicts on a   # different partition of data.   # Step 3: Map the Predictor over the Dataset to get predictions.   predictions = ds.map_batches(HuggingFacePredictor, concurrency=2)   # Step 4: Show one prediction output.   predictions.show(limit=1)              

Learn more about Ray Data Examples

  from ray.train import ScalingConfig   from ray.train.torch import TorchTrainer    # Step 1: Set up PyTorch model training as you normally would.   def train_func():       model = ...       train_dataset = ...       for epoch in range(num_epochs):           ...  # model training logic    # Step 2: Set up Ray's PyTorch Trainer to run on 32 GPUs.   trainer = TorchTrainer(       train_loop_per_worker=train_func,       scaling_config=ScalingConfig(num_workers=32, use_gpu=True),       datasets={"train": train_dataset},   )    # Step 3: Run distributed model training on 32 GPUs.   result = trainer.fit()            

Learn more about Ray Train Examples

  from ray import tune   from ray.train import ScalingConfig   from ray.train.lightgbm import LightGBMTrainer    train_dataset, eval_dataset = ...    # Step 1: Set up Ray's LightGBM Trainer to train on 64 CPUs.   trainer = LightGBMTrainer(       ...       scaling_config=ScalingConfig(num_workers=64),       datasets={"train": train_dataset, "eval": eval_dataset},   )    # Step 2: Set up Ray Tuner to run 1000 trials.   tuner = tune.Tuner(       trainer=trainer,       param_space=hyper_param_space,       tune_config=tune.TuneConfig(num_samples=1000),   )    # Step 3: Run distributed HPO with 1000 trials; each trial runs on 64 CPUs.   result_grid = tuner.fit()            

Learn more about Ray Tune Examples

  from io import BytesIO   from fastapi import FastAPI   from fastapi.responses import Response   import torch    from ray import serve   from ray.serve.handle import DeploymentHandle     app = FastAPI()     @serve.deployment(num_replicas=1)   @serve.ingress(app)   class APIIngress:       def __init__(self, diffusion_model_handle: DeploymentHandle) -> None:           self.handle = diffusion_model_handle        @app.get(           "/imagine",           responses={200: {"content": {"image/png": {}}}},           response_class=Response,       )       async def generate(self, prompt: str, img_size: int = 512):           assert len(prompt), "prompt parameter cannot be empty"            image = await self.handle.generate.remote(prompt, img_size=img_size)           file_stream = BytesIO()           image.save(file_stream, "PNG")           return Response(content=file_stream.getvalue(), media_type="image/png")     @serve.deployment(       ray_actor_options={"num_gpus": 1},       autoscaling_config={"min_replicas": 0, "max_replicas": 2},   )   class StableDiffusionV2:       def __init__(self):           from diffusers import EulerDiscreteScheduler, StableDiffusionPipeline            model_id = "stabilityai/stable-diffusion-2"            scheduler = EulerDiscreteScheduler.from_pretrained(               model_id, subfolder="scheduler"           )           self.pipe = StableDiffusionPipeline.from_pretrained(               model_id, scheduler=scheduler, revision="fp16", torch_dtype=torch.float16           )           self.pipe = self.pipe.to("cuda")        def generate(self, prompt: str, img_size: int = 512):           assert len(prompt), "prompt parameter cannot be empty"            with torch.autocast("cuda"):               image = self.pipe(prompt, height=img_size, width=img_size).images[0]               return image     entrypoint = APIIngress.bind(StableDiffusionV2.bind())            

Learn more about Ray Serve Quickstart

  from ray.rllib.algorithms.ppo import PPOConfig    # Step 1: Configure PPO to run 64 parallel workers to collect samples from the env.   ppo_config = (       PPOConfig()       .environment(env="Taxi-v3")       .rollouts(num_rollout_workers=64)       .framework("torch")       .training(model=rnn_lage)   )    # Step 2: Build the PPO algorithm.   ppo_algo = ppo_config.build()    # Step 3: Train and evaluate PPO.   for _ in range(5):       print(ppo_algo.train())    ppo_algo.evaluate()            

Learn more about Ray RLlib Examples

Beyond the basics

Ray Libraries

Scale the entire ML pipeline from data ingest to model serving with high-level Python APIs that integrate with popular ecosystem frameworks.

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Ray Core

Scale generic Python code with simple, foundational primitives that enable a high degree of control for building distributed applications or custom platforms.

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Ray Clusters

Deploy a Ray cluster on AWS, GCP, Azure, or Kubernetes to seamlessly scale workloads for production.

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